The Conductance Ratio Method for Off-Design Heat Exchanger Modeling and its Impact on an sCO2 Recompression Cycle
Bibliographic record
Abstract
This paper presents a method to evaluate the off-design performance of a heat exchanger without specifying detailed heat exchanger geometry. Presently, off-design heat exchanger performance evaluation is often done by assuming one of the terms in a lumped volume approach is constant (such as UA, temperature difference, ε etc.) or by producing a draft heat exchanger geometry to evaluate the local heat transfer coefficients in off-design operation. As opposed to these approaches, the method presented in this paper manages to accurately predict off-design heat exchanger performance with very limited information. The method relies on a single parameter beyond the design operating conditions, namely the conductance ratio which is the product of heat transfer coefficient and area on both sides of the heat exchanger. The method is particularly powerful as it allows for the exploration of different off-design scenarios for a given on-design operating point. The paper presents a theoretical introduction of the method along with a validation using data provided by BMPC and Alfa Laval for different types of heat exchangers and working fluids, including supercritical CO2. The method is then used to model the off-design performance of a simple recuperated sCO2 cycle, showing its ability to capture the off-design performance of a heat exchanger without specifying its detailed geometry and the impact of conductance ratio on off-design cycle performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".